Project Summary Abstract: National-level data for gender minorities? health and healthcare use is scarce, in part because measures of gender minority identity are rarely collected in national health surveys, electronic health records, or administrative databases. Emerging early evidence in this under-studied population shows that gender minority persons (i.e., transgender or gender non-binary persons; GMs henceforth) experience high mental and physical health burdens and high levels of discrimination in medical settings. Such findings prompted the National Institutes of Health (NIH) to designate GMs as a health disparity population in 2016, calling for increased data collection about GM identities and analyses of GM health and healthcare disparities. Healthcare disparities research about disabled and/or elderly GMs is particularly scarce, despite the fact that they appear to be at especially high risk for depression and chronic illness. There is no consensus methodology for measuring GM healthcare disparities, which may weaken efforts to identify and reduce disparities. Using a novel algorithm to identify elderly and/or disabled GMs in Medicare claims (capturing those who are medically transitioning or ever diagnosed with Gender Identity Disorder [GID]), this project characterizes service use disparities between GM and non-GM patients on (a) treatment for diagnosed depression and (b) use of medical care among patients with chronic conditions. In Aim 1, we rigorously measure healthcare use disparities for GM vs. non-GM Medicare beneficiaries who are elderly and/or disabled by adapting a theoretically grounded healthcare disparities definition originally developed to measure racial/ethnic disparities. This methodology measures disparities by separating the differences in healthcare use that arise from differences in clinical need or patient preferences from the ?unallowable? differences (i.e., disparities) that are mediated through factors such as discrimination, socioeconomic status, and structural healthcare system factors. These methods require non-linear modeling strategies and transformation of covariate distributions. In Aim 2, we quantify what portion of the observed disparity is explained by factors at the individual- and area-level, including race/ethnicity, SES, neighborhood composition, and healthcare supply. Applying the IOM framework to GM disparities research has the potential to unify research methods in GM disparities studies, facilitating comparisons across studies and across healthcare measures so that disparities can be tracked over time. Decomposing the measured disparities into component parts may help identify targets for clinical and policy reform.